Modeling Single-Cell Dynamics Using Unbalanced Parameterized Monge Maps is a research paper (2022). On theSindex it has a DataRank of 0.301. It has been cited 5 times, with 2 citing works in its 1-hop citation network.
Optimal Transport (OT) has proven useful to infer single-cell trajectories of developing biological systems by aligning distributions across time points. Recently, Parameterized Monge Maps (PMM) were introduced to learn the optimal map between two distributions. Here, we apply PMM to model single-cell dynamics and show that PMM fails to account for asymmetric shifts in cell state distributions. To alleviate this limitation, we propose Unbalanced Parameterised Monge Maps (UPMM). We first describe the novel formulation and show on synthetic data how our method extends discrete unbalanced OT to the continuous domain. Then, we demonstrate that UPMM outperforms well-established trajectory inference methods on real-world developmental single-cell data.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.269
From this paper's citation signal
Citation Network Contribution
0.0321
From 2 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 89% comes from its base citations and 11% from the citation network (2 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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